In a Digital Television (DTV) system, the signal at the receiver often includes distortions introduced by a transmitter, a transmission channel and/or the receiver itself. Consequently, DTV receivers normally include an equalizer to remove linear distortions. The equalizer may be an adaptive equalizer, i.e., one which employs an equalizer adaptation method that is responsive to the differences (“error information”) between the equalizer's output and the transmitted DTV signal. The error information is calculated by subtracting the equalizer output from the received signal. An adaptive equalizer typically has tap values or coefficients.
The DTV signal reception process can be divided into two phases: signal acquisition and signal tracking. During the tracking phase, which is the phase after the system has solidly acquired the DTV signal, equalizer adaptation is “blindly” maintained by the use of Viterbi decoder “soft decisions”. Soft-decision Viterbi decoders maintain a history of many possible transmitted sequences, building up a view of their relative likelihoods and finally selecting the value of logic 0 or 1 for each bit according to which has the maximum likelihood. Viterbi soft decisions are typically 8-VSB constellations which are mapped from the corresponding Viterbi decoded bits.
During the acquisition phase, which is the period of time when Viterbi decoder decisions are not yet reliable, a training sequence is often used to initiate the adaptive equalizer. For example, the 8-VSB Advanced Television Systems Committee (ATSC) signal employed by the United States' ATSC digital television system includes a Data Field Sync (DFS) training signal, whose length is 820 symbols. This DFS training signal is repetitively transmitted every 313 DTV segments. Prior art solutions employ the DFS training signal to initiate the adaptive equalizer during the training signal period. However, in the presence of severe multi-path conditions, the training signal period is often too short for the equalizer to converge to a correct solution. This results in an unsuccessful transition between the acquisition phase and the tracking phase using Viterbi decoder soft decisions to drive equalizer adaptation.
One solution implements a Least Mean Squares (LMS) algorithm. This solution includes performing equalizer initialization by employing the LMS algorithm on a training signal, followed by “blind” LMS processing (i.e. without a training signal). Calculations using the LMS algorithm take a long time to converge when strong echoes are present. To allow the LMS algorithm to converge, a low learning rate, which means small incremental steps in each adaptation stage of the LMS algorithm, must be used. However, if these incremental steps are small, the process will never converge when multipath conditions are changed during the calculation. A Recursive Least Squares (RLS) algorithm can also be used to determine equalizer tap values. Examples of an RLS algorithm include Fast Transversal Filter (FTF) method, Fast RLS method and the Fast-Kalman method. Generally these methods, although computationally efficient, suffer from numerical instabilities. However, an RLS algorithm is expensive to implement.
Other methods of initializing equalizer tap values include the use of Reduced Constellation Algorithm (RCA) LMS. This implementation utilizes only the sign of a decision signal from an equalizer, but not the value of the decision signal. The RCA LMS also assumes there is no error in the sign and that the transmitter is using Binary Phase Shift Keying, with only two signal magnitudes.
Calculation of equalizer tap values can also be achieved by implementing a Constant Modulus Algorithm (CMA). This algorithm uses the magnitude of the decision signal, not the sign or phase, of the constellation and assumes the two level constellation is transmitted with different amplitudes. In this case also, the needed information is not in the sign of the decision, but in the amplitude.
One conventional solution divides the equalizer tap values into two groups and utilizes the RLS algorithm. The solution proposes a reduced complexity solution for a feed-forward equalizer (FFE) unit tap values by constraining the length of a feed-back equalizer (FBE) unit. Another solution separates the received multipath echoes into two groups, major and minor equalizer tap values. The method of equalization used to separate the received multipath echoes is a frequency domain filter.
Yet another solution deals with frequency domain equalization for discrete multitone modulation (DMT) or orthogonal frequency-division multiplexing (OFDM) signal. Each frequency domain sub-symbol is equalized by its own “multi-tap” filter. Each filter is updated at each symbol ‘k’, by using RLS. The idea in this approach is that, in contrast to regular RLS, all the Kalman gain coefficients, except for one, are common for all the filters, and changed only from symbol to symbol. This is like LMS, in which the Kalman gain reduces a constant vector of constant step size. A ‘Per Tone Equalizer’ (PTEQ) system provides a distinct equalizer for each carrier tone of 256 carriers in a DMT channel. This adaptation initialization scheme for this PTEQ is based on a combination of LMS and RLS, with inverse updating and the equalizer tap values are falling into two categories, tone independent and tone dependent.
None of these solutions, however, provides a desirable balance of fast convergence to a solution and efficient processing resource utilization.